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Author*The author of this computation has been verified*
R Software Modulerwasp_structuraltimeseries.wasp
Title produced by softwareStructural Time Series Models
Date of computationTue, 28 Dec 2010 01:36:48 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/28/t129350014487jcj056k1nyaxf.htm/, Retrieved Sat, 04 May 2024 20:24:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116209, Retrieved Sat, 04 May 2024 20:24:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Structural Time Series Models] [STSM - werkloosheid] [2010-12-20 13:31:59] [e7fc384c3b263e46f871dfcba42cc90e]
-    D    [Structural Time Series Models] [Paper: Structural...] [2010-12-28 01:36:48] [5876f3b3a8c6f0cebdbe74121f58174b] [Current]
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Dataseries X:
508643
527568
520008
498484
523917
553522
558901
548933
567013
551085
588245
605010
631572
639180
653847
657073
626291
625616
633352
672820
691369
702595
692241
718722
732297
721798
766192
788456
806132
813944
788025
765985
702684
730159
678942
672527
594783
594575
576299
530770
524491
456590
428448
444937
372206
317272
297604
288561
289287
258923
255493
277992
295474
291680
318736
338463
351963
347240
347081
383486




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116209&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116209&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116209&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ 72.249.76.132







Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1508643508643000
2527568522879.7379072612235.54280668410880.5702673952390.679303067014587
3520008520796.001371167996.795015088428765.275075422108-0.243427050519995
4498484503560.94062598-5295.81893424825732.968754851264-0.947006177484237
5523917517414.2325427591636.66492039466794.9865767115660.953082098762873
6553522545188.49762713511212.4883283357700.2943074053671.28471769597676
7558901557855.49221038111747.1124366646622.5840099503720.0713250413239441
8548933553110.1230130285677.75448637661616.737770967309-0.808827985701807
9567013564625.0465324527827.16489197715690.682500920350.286389305936121
10551085555423.9750524091554.77422647304613.855402129362-0.83572580679396
11588245580623.74089699810266.1205282576742.551714506391.16069288393369
12605010601310.49398228914105.7163429042667.6520227249420.511585887166022
13631572628239.79330681118596.4610658461-210.3066442245390.729299621435798
14639180640578.32237632116435.4087605407-129.931498527007-0.250167206246470
15653847654652.2086059515577.1458193328-126.925143892356-0.115418825504425
16657073660253.15292657811899.5042631907-287.83803785221-0.489106217650624
17626291636772.459353388-1141.36001055664-254.148786458373-1.73060835486476
18625616627864.302269351-4000.86459090566-1.34669412313031-0.380141688125085
19633352631216.058422033-1294.702032838995.986993057464850.360240919284134
20672820663172.17038918110944.75469329267.161509649550541.63027772212875
21691369687487.85503241115867.46256818482.597645506322680.655838226061422
22702595702948.88515119115717.8059228557-235.961903076208-0.0199396744163479
23692241698303.1697534268218.82534935807-153.130575179061-0.99915478906515
24718722716007.34379540111711.7910413418-37.73757663976370.465400457032939
25732297729951.72323177212514.03244326131713.065357064410.118683922536796
26721798726110.5898126616740.59227656606-516.813170496904-0.70603027917733
27766192758782.00047185616199.5968023204-41.45859784022951.26811487042180
28788456785645.89354837220127.4105096696-274.9104033987280.522730032274457
29806132806421.22665931220366.0359282316-476.2855873315380.0317093666897173
30813944817089.97161170416797.3256909277-344.820344599108-0.474776398854136
31788025798809.7724134973891.14654477199-643.309830327182-1.71856979833561
32765985774530.683132659-6474.38151495653-396.491162230097-1.38081451977589
33702684717758.709195631-24984.2587289551-519.882835634134-2.46609516221689
34730159721709.675032519-14334.807722291074.87310425707491.41890446246529
35678942685814.728003832-22270.4212982397-632.260199730614-1.05733375108728
36672527670590.065285948-19677.3728513075-102.2016613747640.345497027198647
37594783610567.894496334-34293.0198164359-4281.86959513517-2.08982269214111
38594575590641.630655958-29166.994563884389.5600345211410.642464534745484
39576299572304.346249627-25210.7169378212886.0458108305440.529598517436605
40530770534077.4971425-30001.3836008155449.299166231358-0.637781472220461
41524491519411.797332152-24357.0622374916659.8039886738350.750521747724276
42456590464867.87315356-35460.9133840915424.736430042922-1.47777298827776
43428448428260.566477481-35882.5311876229518.152288689237-0.0561502261798934
44444937432125.601786134-21263.44007472381339.688568893851.94753762706702
45372206381139.297982589-32196.5266642407-352.814968523353-1.45665169505806
46317272323629.368723455-41508.3908607915951.078195664782-1.24069340101103
47297604293847.217310637-37194.5227785536370.9856908183260.574776418881518
48288561280459.431575179-28438.04000073761228.977656283881.16671956217339
49289287282962.702731473-17185.4437558441-2519.959640732061.58048912720570
50258923260483.862645361-19085.9787334948-209.332049499595-0.241580024153711
51255493251752.16391006-15300.9673259458773.4971158576670.506220109776332
52277992268540.725016916-3497.46645586085209.9552519140431.57174074866072
53295474288103.8552045344985.10703724913737.6686390605261.12834901281228
54291680292048.0986895744602.41929475903-68.6415006026201-0.050941238873255
55318736313933.49695340710955.8986525835-172.5745559184050.84621538156101
56338463334477.11100657914480.64807266261224.968050283030.469576478158825
57351963351678.88437628915481.1004640056-499.6246236941920.133294815364596
58347240351511.9629336369727.55428130257235.448915146358-0.766592517058976
59347081350508.5714194315781.78484233479-336.366757860426-0.525731564377954
60383486376720.50020926813292.0990938283881.82455122651.0006922242197

\begin{tabular}{lllllllll}
\hline
Structural Time Series Model \tabularnewline
t & Observed & Level & Slope & Seasonal & Stand. Residuals \tabularnewline
1 & 508643 & 508643 & 0 & 0 & 0 \tabularnewline
2 & 527568 & 522879.737907261 & 2235.54280668410 & 880.570267395239 & 0.679303067014587 \tabularnewline
3 & 520008 & 520796.001371167 & 996.795015088428 & 765.275075422108 & -0.243427050519995 \tabularnewline
4 & 498484 & 503560.94062598 & -5295.81893424825 & 732.968754851264 & -0.947006177484237 \tabularnewline
5 & 523917 & 517414.232542759 & 1636.66492039466 & 794.986576711566 & 0.953082098762873 \tabularnewline
6 & 553522 & 545188.497627135 & 11212.4883283357 & 700.294307405367 & 1.28471769597676 \tabularnewline
7 & 558901 & 557855.492210381 & 11747.1124366646 & 622.584009950372 & 0.0713250413239441 \tabularnewline
8 & 548933 & 553110.123013028 & 5677.75448637661 & 616.737770967309 & -0.808827985701807 \tabularnewline
9 & 567013 & 564625.046532452 & 7827.16489197715 & 690.68250092035 & 0.286389305936121 \tabularnewline
10 & 551085 & 555423.975052409 & 1554.77422647304 & 613.855402129362 & -0.83572580679396 \tabularnewline
11 & 588245 & 580623.740896998 & 10266.1205282576 & 742.55171450639 & 1.16069288393369 \tabularnewline
12 & 605010 & 601310.493982289 & 14105.7163429042 & 667.652022724942 & 0.511585887166022 \tabularnewline
13 & 631572 & 628239.793306811 & 18596.4610658461 & -210.306644224539 & 0.729299621435798 \tabularnewline
14 & 639180 & 640578.322376321 & 16435.4087605407 & -129.931498527007 & -0.250167206246470 \tabularnewline
15 & 653847 & 654652.20860595 & 15577.1458193328 & -126.925143892356 & -0.115418825504425 \tabularnewline
16 & 657073 & 660253.152926578 & 11899.5042631907 & -287.83803785221 & -0.489106217650624 \tabularnewline
17 & 626291 & 636772.459353388 & -1141.36001055664 & -254.148786458373 & -1.73060835486476 \tabularnewline
18 & 625616 & 627864.302269351 & -4000.86459090566 & -1.34669412313031 & -0.380141688125085 \tabularnewline
19 & 633352 & 631216.058422033 & -1294.70203283899 & 5.98699305746485 & 0.360240919284134 \tabularnewline
20 & 672820 & 663172.170389181 & 10944.7546932926 & 7.16150964955054 & 1.63027772212875 \tabularnewline
21 & 691369 & 687487.855032411 & 15867.4625681848 & 2.59764550632268 & 0.655838226061422 \tabularnewline
22 & 702595 & 702948.885151191 & 15717.8059228557 & -235.961903076208 & -0.0199396744163479 \tabularnewline
23 & 692241 & 698303.169753426 & 8218.82534935807 & -153.130575179061 & -0.99915478906515 \tabularnewline
24 & 718722 & 716007.343795401 & 11711.7910413418 & -37.7375766397637 & 0.465400457032939 \tabularnewline
25 & 732297 & 729951.723231772 & 12514.0324432613 & 1713.06535706441 & 0.118683922536796 \tabularnewline
26 & 721798 & 726110.589812661 & 6740.59227656606 & -516.813170496904 & -0.70603027917733 \tabularnewline
27 & 766192 & 758782.000471856 & 16199.5968023204 & -41.4585978402295 & 1.26811487042180 \tabularnewline
28 & 788456 & 785645.893548372 & 20127.4105096696 & -274.910403398728 & 0.522730032274457 \tabularnewline
29 & 806132 & 806421.226659312 & 20366.0359282316 & -476.285587331538 & 0.0317093666897173 \tabularnewline
30 & 813944 & 817089.971611704 & 16797.3256909277 & -344.820344599108 & -0.474776398854136 \tabularnewline
31 & 788025 & 798809.772413497 & 3891.14654477199 & -643.309830327182 & -1.71856979833561 \tabularnewline
32 & 765985 & 774530.683132659 & -6474.38151495653 & -396.491162230097 & -1.38081451977589 \tabularnewline
33 & 702684 & 717758.709195631 & -24984.2587289551 & -519.882835634134 & -2.46609516221689 \tabularnewline
34 & 730159 & 721709.675032519 & -14334.8077222910 & 74.8731042570749 & 1.41890446246529 \tabularnewline
35 & 678942 & 685814.728003832 & -22270.4212982397 & -632.260199730614 & -1.05733375108728 \tabularnewline
36 & 672527 & 670590.065285948 & -19677.3728513075 & -102.201661374764 & 0.345497027198647 \tabularnewline
37 & 594783 & 610567.894496334 & -34293.0198164359 & -4281.86959513517 & -2.08982269214111 \tabularnewline
38 & 594575 & 590641.630655958 & -29166.994563884 & 389.560034521141 & 0.642464534745484 \tabularnewline
39 & 576299 & 572304.346249627 & -25210.7169378212 & 886.045810830544 & 0.529598517436605 \tabularnewline
40 & 530770 & 534077.4971425 & -30001.3836008155 & 449.299166231358 & -0.637781472220461 \tabularnewline
41 & 524491 & 519411.797332152 & -24357.0622374916 & 659.803988673835 & 0.750521747724276 \tabularnewline
42 & 456590 & 464867.87315356 & -35460.9133840915 & 424.736430042922 & -1.47777298827776 \tabularnewline
43 & 428448 & 428260.566477481 & -35882.5311876229 & 518.152288689237 & -0.0561502261798934 \tabularnewline
44 & 444937 & 432125.601786134 & -21263.4400747238 & 1339.68856889385 & 1.94753762706702 \tabularnewline
45 & 372206 & 381139.297982589 & -32196.5266642407 & -352.814968523353 & -1.45665169505806 \tabularnewline
46 & 317272 & 323629.368723455 & -41508.3908607915 & 951.078195664782 & -1.24069340101103 \tabularnewline
47 & 297604 & 293847.217310637 & -37194.5227785536 & 370.985690818326 & 0.574776418881518 \tabularnewline
48 & 288561 & 280459.431575179 & -28438.0400007376 & 1228.97765628388 & 1.16671956217339 \tabularnewline
49 & 289287 & 282962.702731473 & -17185.4437558441 & -2519.95964073206 & 1.58048912720570 \tabularnewline
50 & 258923 & 260483.862645361 & -19085.9787334948 & -209.332049499595 & -0.241580024153711 \tabularnewline
51 & 255493 & 251752.16391006 & -15300.9673259458 & 773.497115857667 & 0.506220109776332 \tabularnewline
52 & 277992 & 268540.725016916 & -3497.46645586085 & 209.955251914043 & 1.57174074866072 \tabularnewline
53 & 295474 & 288103.855204534 & 4985.10703724913 & 737.668639060526 & 1.12834901281228 \tabularnewline
54 & 291680 & 292048.098689574 & 4602.41929475903 & -68.6415006026201 & -0.050941238873255 \tabularnewline
55 & 318736 & 313933.496953407 & 10955.8986525835 & -172.574555918405 & 0.84621538156101 \tabularnewline
56 & 338463 & 334477.111006579 & 14480.6480726626 & 1224.96805028303 & 0.469576478158825 \tabularnewline
57 & 351963 & 351678.884376289 & 15481.1004640056 & -499.624623694192 & 0.133294815364596 \tabularnewline
58 & 347240 & 351511.962933636 & 9727.55428130257 & 235.448915146358 & -0.766592517058976 \tabularnewline
59 & 347081 & 350508.571419431 & 5781.78484233479 & -336.366757860426 & -0.525731564377954 \tabularnewline
60 & 383486 & 376720.500209268 & 13292.0990938283 & 881.8245512265 & 1.0006922242197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116209&T=1

[TABLE]
[ROW][C]Structural Time Series Model[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Level[/C][C]Slope[/C][C]Seasonal[/C][C]Stand. Residuals[/C][/ROW]
[ROW][C]1[/C][C]508643[/C][C]508643[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]527568[/C][C]522879.737907261[/C][C]2235.54280668410[/C][C]880.570267395239[/C][C]0.679303067014587[/C][/ROW]
[ROW][C]3[/C][C]520008[/C][C]520796.001371167[/C][C]996.795015088428[/C][C]765.275075422108[/C][C]-0.243427050519995[/C][/ROW]
[ROW][C]4[/C][C]498484[/C][C]503560.94062598[/C][C]-5295.81893424825[/C][C]732.968754851264[/C][C]-0.947006177484237[/C][/ROW]
[ROW][C]5[/C][C]523917[/C][C]517414.232542759[/C][C]1636.66492039466[/C][C]794.986576711566[/C][C]0.953082098762873[/C][/ROW]
[ROW][C]6[/C][C]553522[/C][C]545188.497627135[/C][C]11212.4883283357[/C][C]700.294307405367[/C][C]1.28471769597676[/C][/ROW]
[ROW][C]7[/C][C]558901[/C][C]557855.492210381[/C][C]11747.1124366646[/C][C]622.584009950372[/C][C]0.0713250413239441[/C][/ROW]
[ROW][C]8[/C][C]548933[/C][C]553110.123013028[/C][C]5677.75448637661[/C][C]616.737770967309[/C][C]-0.808827985701807[/C][/ROW]
[ROW][C]9[/C][C]567013[/C][C]564625.046532452[/C][C]7827.16489197715[/C][C]690.68250092035[/C][C]0.286389305936121[/C][/ROW]
[ROW][C]10[/C][C]551085[/C][C]555423.975052409[/C][C]1554.77422647304[/C][C]613.855402129362[/C][C]-0.83572580679396[/C][/ROW]
[ROW][C]11[/C][C]588245[/C][C]580623.740896998[/C][C]10266.1205282576[/C][C]742.55171450639[/C][C]1.16069288393369[/C][/ROW]
[ROW][C]12[/C][C]605010[/C][C]601310.493982289[/C][C]14105.7163429042[/C][C]667.652022724942[/C][C]0.511585887166022[/C][/ROW]
[ROW][C]13[/C][C]631572[/C][C]628239.793306811[/C][C]18596.4610658461[/C][C]-210.306644224539[/C][C]0.729299621435798[/C][/ROW]
[ROW][C]14[/C][C]639180[/C][C]640578.322376321[/C][C]16435.4087605407[/C][C]-129.931498527007[/C][C]-0.250167206246470[/C][/ROW]
[ROW][C]15[/C][C]653847[/C][C]654652.20860595[/C][C]15577.1458193328[/C][C]-126.925143892356[/C][C]-0.115418825504425[/C][/ROW]
[ROW][C]16[/C][C]657073[/C][C]660253.152926578[/C][C]11899.5042631907[/C][C]-287.83803785221[/C][C]-0.489106217650624[/C][/ROW]
[ROW][C]17[/C][C]626291[/C][C]636772.459353388[/C][C]-1141.36001055664[/C][C]-254.148786458373[/C][C]-1.73060835486476[/C][/ROW]
[ROW][C]18[/C][C]625616[/C][C]627864.302269351[/C][C]-4000.86459090566[/C][C]-1.34669412313031[/C][C]-0.380141688125085[/C][/ROW]
[ROW][C]19[/C][C]633352[/C][C]631216.058422033[/C][C]-1294.70203283899[/C][C]5.98699305746485[/C][C]0.360240919284134[/C][/ROW]
[ROW][C]20[/C][C]672820[/C][C]663172.170389181[/C][C]10944.7546932926[/C][C]7.16150964955054[/C][C]1.63027772212875[/C][/ROW]
[ROW][C]21[/C][C]691369[/C][C]687487.855032411[/C][C]15867.4625681848[/C][C]2.59764550632268[/C][C]0.655838226061422[/C][/ROW]
[ROW][C]22[/C][C]702595[/C][C]702948.885151191[/C][C]15717.8059228557[/C][C]-235.961903076208[/C][C]-0.0199396744163479[/C][/ROW]
[ROW][C]23[/C][C]692241[/C][C]698303.169753426[/C][C]8218.82534935807[/C][C]-153.130575179061[/C][C]-0.99915478906515[/C][/ROW]
[ROW][C]24[/C][C]718722[/C][C]716007.343795401[/C][C]11711.7910413418[/C][C]-37.7375766397637[/C][C]0.465400457032939[/C][/ROW]
[ROW][C]25[/C][C]732297[/C][C]729951.723231772[/C][C]12514.0324432613[/C][C]1713.06535706441[/C][C]0.118683922536796[/C][/ROW]
[ROW][C]26[/C][C]721798[/C][C]726110.589812661[/C][C]6740.59227656606[/C][C]-516.813170496904[/C][C]-0.70603027917733[/C][/ROW]
[ROW][C]27[/C][C]766192[/C][C]758782.000471856[/C][C]16199.5968023204[/C][C]-41.4585978402295[/C][C]1.26811487042180[/C][/ROW]
[ROW][C]28[/C][C]788456[/C][C]785645.893548372[/C][C]20127.4105096696[/C][C]-274.910403398728[/C][C]0.522730032274457[/C][/ROW]
[ROW][C]29[/C][C]806132[/C][C]806421.226659312[/C][C]20366.0359282316[/C][C]-476.285587331538[/C][C]0.0317093666897173[/C][/ROW]
[ROW][C]30[/C][C]813944[/C][C]817089.971611704[/C][C]16797.3256909277[/C][C]-344.820344599108[/C][C]-0.474776398854136[/C][/ROW]
[ROW][C]31[/C][C]788025[/C][C]798809.772413497[/C][C]3891.14654477199[/C][C]-643.309830327182[/C][C]-1.71856979833561[/C][/ROW]
[ROW][C]32[/C][C]765985[/C][C]774530.683132659[/C][C]-6474.38151495653[/C][C]-396.491162230097[/C][C]-1.38081451977589[/C][/ROW]
[ROW][C]33[/C][C]702684[/C][C]717758.709195631[/C][C]-24984.2587289551[/C][C]-519.882835634134[/C][C]-2.46609516221689[/C][/ROW]
[ROW][C]34[/C][C]730159[/C][C]721709.675032519[/C][C]-14334.8077222910[/C][C]74.8731042570749[/C][C]1.41890446246529[/C][/ROW]
[ROW][C]35[/C][C]678942[/C][C]685814.728003832[/C][C]-22270.4212982397[/C][C]-632.260199730614[/C][C]-1.05733375108728[/C][/ROW]
[ROW][C]36[/C][C]672527[/C][C]670590.065285948[/C][C]-19677.3728513075[/C][C]-102.201661374764[/C][C]0.345497027198647[/C][/ROW]
[ROW][C]37[/C][C]594783[/C][C]610567.894496334[/C][C]-34293.0198164359[/C][C]-4281.86959513517[/C][C]-2.08982269214111[/C][/ROW]
[ROW][C]38[/C][C]594575[/C][C]590641.630655958[/C][C]-29166.994563884[/C][C]389.560034521141[/C][C]0.642464534745484[/C][/ROW]
[ROW][C]39[/C][C]576299[/C][C]572304.346249627[/C][C]-25210.7169378212[/C][C]886.045810830544[/C][C]0.529598517436605[/C][/ROW]
[ROW][C]40[/C][C]530770[/C][C]534077.4971425[/C][C]-30001.3836008155[/C][C]449.299166231358[/C][C]-0.637781472220461[/C][/ROW]
[ROW][C]41[/C][C]524491[/C][C]519411.797332152[/C][C]-24357.0622374916[/C][C]659.803988673835[/C][C]0.750521747724276[/C][/ROW]
[ROW][C]42[/C][C]456590[/C][C]464867.87315356[/C][C]-35460.9133840915[/C][C]424.736430042922[/C][C]-1.47777298827776[/C][/ROW]
[ROW][C]43[/C][C]428448[/C][C]428260.566477481[/C][C]-35882.5311876229[/C][C]518.152288689237[/C][C]-0.0561502261798934[/C][/ROW]
[ROW][C]44[/C][C]444937[/C][C]432125.601786134[/C][C]-21263.4400747238[/C][C]1339.68856889385[/C][C]1.94753762706702[/C][/ROW]
[ROW][C]45[/C][C]372206[/C][C]381139.297982589[/C][C]-32196.5266642407[/C][C]-352.814968523353[/C][C]-1.45665169505806[/C][/ROW]
[ROW][C]46[/C][C]317272[/C][C]323629.368723455[/C][C]-41508.3908607915[/C][C]951.078195664782[/C][C]-1.24069340101103[/C][/ROW]
[ROW][C]47[/C][C]297604[/C][C]293847.217310637[/C][C]-37194.5227785536[/C][C]370.985690818326[/C][C]0.574776418881518[/C][/ROW]
[ROW][C]48[/C][C]288561[/C][C]280459.431575179[/C][C]-28438.0400007376[/C][C]1228.97765628388[/C][C]1.16671956217339[/C][/ROW]
[ROW][C]49[/C][C]289287[/C][C]282962.702731473[/C][C]-17185.4437558441[/C][C]-2519.95964073206[/C][C]1.58048912720570[/C][/ROW]
[ROW][C]50[/C][C]258923[/C][C]260483.862645361[/C][C]-19085.9787334948[/C][C]-209.332049499595[/C][C]-0.241580024153711[/C][/ROW]
[ROW][C]51[/C][C]255493[/C][C]251752.16391006[/C][C]-15300.9673259458[/C][C]773.497115857667[/C][C]0.506220109776332[/C][/ROW]
[ROW][C]52[/C][C]277992[/C][C]268540.725016916[/C][C]-3497.46645586085[/C][C]209.955251914043[/C][C]1.57174074866072[/C][/ROW]
[ROW][C]53[/C][C]295474[/C][C]288103.855204534[/C][C]4985.10703724913[/C][C]737.668639060526[/C][C]1.12834901281228[/C][/ROW]
[ROW][C]54[/C][C]291680[/C][C]292048.098689574[/C][C]4602.41929475903[/C][C]-68.6415006026201[/C][C]-0.050941238873255[/C][/ROW]
[ROW][C]55[/C][C]318736[/C][C]313933.496953407[/C][C]10955.8986525835[/C][C]-172.574555918405[/C][C]0.84621538156101[/C][/ROW]
[ROW][C]56[/C][C]338463[/C][C]334477.111006579[/C][C]14480.6480726626[/C][C]1224.96805028303[/C][C]0.469576478158825[/C][/ROW]
[ROW][C]57[/C][C]351963[/C][C]351678.884376289[/C][C]15481.1004640056[/C][C]-499.624623694192[/C][C]0.133294815364596[/C][/ROW]
[ROW][C]58[/C][C]347240[/C][C]351511.962933636[/C][C]9727.55428130257[/C][C]235.448915146358[/C][C]-0.766592517058976[/C][/ROW]
[ROW][C]59[/C][C]347081[/C][C]350508.571419431[/C][C]5781.78484233479[/C][C]-336.366757860426[/C][C]-0.525731564377954[/C][/ROW]
[ROW][C]60[/C][C]383486[/C][C]376720.500209268[/C][C]13292.0990938283[/C][C]881.8245512265[/C][C]1.0006922242197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116209&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116209&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Structural Time Series Model
tObservedLevelSlopeSeasonalStand. Residuals
1508643508643000
2527568522879.7379072612235.54280668410880.5702673952390.679303067014587
3520008520796.001371167996.795015088428765.275075422108-0.243427050519995
4498484503560.94062598-5295.81893424825732.968754851264-0.947006177484237
5523917517414.2325427591636.66492039466794.9865767115660.953082098762873
6553522545188.49762713511212.4883283357700.2943074053671.28471769597676
7558901557855.49221038111747.1124366646622.5840099503720.0713250413239441
8548933553110.1230130285677.75448637661616.737770967309-0.808827985701807
9567013564625.0465324527827.16489197715690.682500920350.286389305936121
10551085555423.9750524091554.77422647304613.855402129362-0.83572580679396
11588245580623.74089699810266.1205282576742.551714506391.16069288393369
12605010601310.49398228914105.7163429042667.6520227249420.511585887166022
13631572628239.79330681118596.4610658461-210.3066442245390.729299621435798
14639180640578.32237632116435.4087605407-129.931498527007-0.250167206246470
15653847654652.2086059515577.1458193328-126.925143892356-0.115418825504425
16657073660253.15292657811899.5042631907-287.83803785221-0.489106217650624
17626291636772.459353388-1141.36001055664-254.148786458373-1.73060835486476
18625616627864.302269351-4000.86459090566-1.34669412313031-0.380141688125085
19633352631216.058422033-1294.702032838995.986993057464850.360240919284134
20672820663172.17038918110944.75469329267.161509649550541.63027772212875
21691369687487.85503241115867.46256818482.597645506322680.655838226061422
22702595702948.88515119115717.8059228557-235.961903076208-0.0199396744163479
23692241698303.1697534268218.82534935807-153.130575179061-0.99915478906515
24718722716007.34379540111711.7910413418-37.73757663976370.465400457032939
25732297729951.72323177212514.03244326131713.065357064410.118683922536796
26721798726110.5898126616740.59227656606-516.813170496904-0.70603027917733
27766192758782.00047185616199.5968023204-41.45859784022951.26811487042180
28788456785645.89354837220127.4105096696-274.9104033987280.522730032274457
29806132806421.22665931220366.0359282316-476.2855873315380.0317093666897173
30813944817089.97161170416797.3256909277-344.820344599108-0.474776398854136
31788025798809.7724134973891.14654477199-643.309830327182-1.71856979833561
32765985774530.683132659-6474.38151495653-396.491162230097-1.38081451977589
33702684717758.709195631-24984.2587289551-519.882835634134-2.46609516221689
34730159721709.675032519-14334.807722291074.87310425707491.41890446246529
35678942685814.728003832-22270.4212982397-632.260199730614-1.05733375108728
36672527670590.065285948-19677.3728513075-102.2016613747640.345497027198647
37594783610567.894496334-34293.0198164359-4281.86959513517-2.08982269214111
38594575590641.630655958-29166.994563884389.5600345211410.642464534745484
39576299572304.346249627-25210.7169378212886.0458108305440.529598517436605
40530770534077.4971425-30001.3836008155449.299166231358-0.637781472220461
41524491519411.797332152-24357.0622374916659.8039886738350.750521747724276
42456590464867.87315356-35460.9133840915424.736430042922-1.47777298827776
43428448428260.566477481-35882.5311876229518.152288689237-0.0561502261798934
44444937432125.601786134-21263.44007472381339.688568893851.94753762706702
45372206381139.297982589-32196.5266642407-352.814968523353-1.45665169505806
46317272323629.368723455-41508.3908607915951.078195664782-1.24069340101103
47297604293847.217310637-37194.5227785536370.9856908183260.574776418881518
48288561280459.431575179-28438.04000073761228.977656283881.16671956217339
49289287282962.702731473-17185.4437558441-2519.959640732061.58048912720570
50258923260483.862645361-19085.9787334948-209.332049499595-0.241580024153711
51255493251752.16391006-15300.9673259458773.4971158576670.506220109776332
52277992268540.725016916-3497.46645586085209.9552519140431.57174074866072
53295474288103.8552045344985.10703724913737.6686390605261.12834901281228
54291680292048.0986895744602.41929475903-68.6415006026201-0.050941238873255
55318736313933.49695340710955.8986525835-172.5745559184050.84621538156101
56338463334477.11100657914480.64807266261224.968050283030.469576478158825
57351963351678.88437628915481.1004640056-499.6246236941920.133294815364596
58347240351511.9629336369727.55428130257235.448915146358-0.766592517058976
59347081350508.5714194315781.78484233479-336.366757860426-0.525731564377954
60383486376720.50020926813292.0990938283881.82455122651.0006922242197



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
nx <- length(x)
x <- ts(x,frequency=par1)
m <- StructTS(x,type='BSM')
m$coef
m$fitted
m$resid
mylevel <- as.numeric(m$fitted[,'level'])
myslope <- as.numeric(m$fitted[,'slope'])
myseas <- as.numeric(m$fitted[,'sea'])
myresid <- as.numeric(m$resid)
myfit <- mylevel+myseas
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(mylevel,na.action=na.pass,lag.max = mylagmax,main='Level')
acf(myseas,na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(myresid,na.action=na.pass,lag.max = mylagmax,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(mylevel,main='Level')
spectrum(myseas,main='Seasonal')
spectrum(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(mylevel,main='Level')
cpgram(myseas,main='Seasonal')
cpgram(myresid,main='Standardized Residals')
par(op)
dev.off()
bitmap(file='test1.png')
plot(as.numeric(m$resid),main='Standardized Residuals',ylab='Residuals',xlab='time',type='b')
grid()
dev.off()
bitmap(file='test5.png')
op <- par(mfrow = c(2,2))
hist(m$resid,main='Residual Histogram')
plot(density(m$resid),main='Residual Kernel Density')
qqnorm(m$resid,main='Residual Normal QQ Plot')
qqline(m$resid)
plot(m$resid^2, myfit^2,main='Sq.Resid vs. Sq.Fit',xlab='Squared residuals',ylab='Squared Fit')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Structural Time Series Model',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Level',header=TRUE)
a<-table.element(a,'Slope',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Stand. Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,mylevel[i])
a<-table.element(a,myslope[i])
a<-table.element(a,myseas[i])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')